A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

Olowolaju, Joshua; Thapa, Jitendra; Hossain, Rakib; Benidris, Mohammed; Livani, Hanif

doi:10.1109/tia.2024.3429477

IEEE Trans. on Ind. Applicat.

2024

DOI: 10.1109/tia.2024.3429477

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A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

Joshua Olowolaju,

Jitendra Thapa,

Rakib Hossain

et al.

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2024

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Cited by 2 publications

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An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

Figueroa,

Bubbar,

Young-Morris

2024

Energies

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This paper introduces an open-source tool capable of performing the Composite System Reliability evaluation developed in the high-level, dynamic Julia™ programming language. Employing Monte Carlo Simulation and parallel computing, the tool evaluates probabilistic adequacy indices for combined generation and transmission systems, focusing on both individual delivery points and the broader system. Proficiency in Optimal Power Flow problem formulations is demonstrated through two distinct methods: DC and linearized AC, enabling comprehensive resource and transmission adequacy analysis with high-performance solvers. Addressing replicability and the insufficiency of available software, the tool supports diverse analyses on a unified platform. The paper discusses the tool’s design and validation, particularly focusing on the two optimal power flow problem formulations. These insights significantly contribute to understanding transmission system performance and have implications for power system planning.

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An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

Figueroa,

Bubbar,

Young-Morris

2024

Energies

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Tensor-Based Predictor–Corrector Algorithm for Power Generation and Transmission Reliability Assessment with Sequential Monte Carlo Simulation

Pequeno dos Santos,

Buss,

Rosa

et al. 2024

Energies

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The reliability assesment of large power systems, particularly when considering both generation and transmission facilities, is a computationally demanding and complex problem. The sequential Monte Carlo simulation is arguably the most versatile approach for tackling this problem. However, assessing sampled states in the sequential Monte Carlo simulation is time-intensive, rendering its use less appealing, particularly if nonlinear network representation must be deployed. In this context, this paper introduces a tensor-based predictor–corrector approach to reduce the burden of state evaluations in power generation and transmission reliability assessments. The approach allows for searching for sequences of operation points which can be assigned as success states within the sequential Monte Carlo simulation. If required, failure states are evaluated using a cross-entropy optimization algorithm designed to minimize load curtailments taking into account discrete variables. Numerical results emphasize the applicability of the developed algorithms using a small test system and the IEEE-RTS79 test system.

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A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

Cited by 2 publications

References 25 publications

An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

Tensor-Based Predictor–Corrector Algorithm for Power Generation and Transmission Reliability Assessment with Sequential Monte Carlo Simulation

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